Meet the innovators, leaders, and thinkers reshaping our world through groundbreaking advances in artificial intelligence. Time’s 100 most influential people in AI of 2025. The list includes familiar names like Sam Altman, Elon Musk, Jensen Huang, and Fei-Fei Li alongside newcomers like DeepSeek CEO Liang Wenfeng. — Read More

The landscape of AI agents has been dominated by large language models (LLMs) like GPT-4 and Claude, but a new frontier is opening up: lightweight, open-source, locally-deployable agents that can run on consumer hardware. This post shares internal notes and discoveries from my journey building agents for small language models (SLMs) – models ranging from 270M to 32B parameters that run efficiently on CPUs or modest GPUs. These are lessons learned from hands-on experimentation, debugging, and optimizing inference pipelines.

SLMs offer immense potential: privacy through local deployment, predictable costs, and full control thanks to open weights. However, they also present unique challenges that demand a shift in how we design agent architectures. — Read More

I was recently trying to convince a friend of mine that ChatGPT hasn’t memorized every possible medical record, and that when she was passing her blood work results the model was doing pattern matching in ways that even OpenAI couldn’t really foresee. She couldn’t believe me, and I totally understand why. It’s hard to accept that we invented a technology that we don’t fully comprehend, and that exhibits behaviors that we didn’t explicitly expect.

Dismissal is a common reaction when witnessing AI’s rate of progress. People struggle to reconcile their world model with what AI can now do, and how.

This isn’t new. Mainstream intuition and cultural impact always lag behind new technical capabilities. When we started building businesses on the Internet three decades ago, the skepticism was similar. Sending checks to strangers and giving away services for free felt absurd. But those who grasped a new reality made of zero marginal costs and infinitely scalable distribution became incredibly wealthy. They understood that the old assumptions baked into their worldview no longer applied, and acted on it.

Eventually the world caught up. — Read More

Artificial General Intelligence (AGI), Artificial Super Intelligence (ASI) and fully general humanoid robotics are just around the corner, or so many people believe. So it’s time to try to understand how this will affect our economy. Will we be forced into lives of idle leisure and/or meaninglessness? Will the few remaining human workers toil below the API? Will we get fully automated luxury gay space communism?

This is a follow up after five years to my original post on post scarcity and post-capitalism.

Keynes predicted in 1930 that by 2030, automation would reduce the need for work to just 15 hours per week. We’re almost there, so what did he get right and wrong? First, most people work fewer hours in less physically taxing jobs than their grandparents did. But we’ve standardized on 40 hour weeks, and much more for people in jobs with a high degree of competition. Within those 40 hours, I am reliably informed, some people struggle to perform even a single hour of meaningful work, but these are not the rule. — Read More

As economists speculate whether the stock market is in an AI bubble that could soon burst, OpenAI CEO Sam Altman has just admitted to believing we’re in one. “Are we in a phase where investors as a whole are overexcited about AI?” Altman said during a lengthy interview with The Verge and other reporters last night. “My opinion is yes.” — Read More

#strategy

There’s something happening in our IDEs that nobody’s talking about.

Not the obvious stuff—everyone sees the autocomplete getting smarter, the boilerplate evaporating, the bugs caught before they hatch. I’m talking about something deeper. Something that makes my Moroccan grandmother’s warnings about djinn possession feel less like folklore and more like… documentation.

We’re witnessing the first large-scale cognitive handoff between human and artificial intelligence. And it’s not just changing how we build software—it’s rewiring how our entire civilization processes information. — Read More

…. Current AIs aren’t AGI. But I don’t know why.

I mean, I have thoughts. I talk about missing functions like “memory” and “continuous learning”, and possibly “judgement” and “insight”. But these are all debatable; for instance, ChatGPT has a form of memory. The honest answer is: I dunno what’s missing, but something is, because there are a lot of things AI still can’t do. Even if it’s getting harder and harder to articulate exactly what those things are. — Read More

This essay dissects the widening gap between AI hype and reality, arguing that large language models have hit a plateau – the “S-curve” – despite industry claims of imminent superintelligence. It contrasts bold predictions and massive investments with underwhelming flagship releases, framing today’s AI era as less about building godlike intelligence and more about integrating imperfect tools into real-world products. The piece suggests that the true future of AI lies not in transcendence, but in the messy, necessary work of making these systems actually useful.

GPT-5 has sealed the deal. It is one in a line of underachieving flagship models from major AI labs. …At the same time, we have major manifests of the world entering an age of superintelligence, in which we either all go extinct like ants getting exterminated by superintelligent “pest control” or we ride a benevolent superintelligence that provides us with a post-scarcity paradise.

… We seem to have both bullish and bearish signals. When push comes to shove, I like to rely on the technological signals over the signals from philosophers or Wall Street.

I believe that AGI is not possible with the current regime of LLMs. The GPT-style autoregressive language transformer that was published in 2018 by OpenAI as GPT-1 – this style of AI, we shall call them LLMs from now – lacks the capabilities needed for AGI. — Read More

OpenAI just released GPT-5—to great fanfare and mixed reviews around the internet. According to benchmarks and subjective personal testing, GPT-5 is better than GPT-4 and o3.

It’s certainly a better default than GPT-4o, which is what most people used on ChatGPT’s interface. The model dominates across the board in LMArena.XXXXI don’t feel it as much. But I also used OpenAI’s research previews of o3-mini-high, GPT-4.5, and other models for specific tasks. As such, I don’t really see it as revolutionary. That makes sense though. Today, if you try to select other models in the Plus subscription, all you get is GPT-5 and GPT-5 Thinking (the latter being the “high effort” version of the first).

The function of those research previews all got rolled into the 5-series. — Read More

The complexity and scale of modern infrastructure requires an equally intelligent set of observability tools to effectively monitor it.

Remember when scaling meant ordering new servers and racking them in a data center? Remember when cloud providers first offered access to seemingly infinite virtual machines at the click of a button? Remember when Kubernetes made it trivial for infrastructure to automatically scale itself based on demand? Artificial intelligence (AI) is now fostering faster software development and more intelligent orchestration, once again exponentially increasing the scale of IT infrastructure.

Welcome to the brave new world of modern observability and infrastructure! If you’re feeling like the ground is shifting beneath your feet as an SRE or IT Operations professional, you’re not alone. The way we build and run systems has undergone a dramatic transformation, and the tools we use to observe these systems need modernization to keep up. This isn’t just an evolution; it’s an “everything changed” moment. — Read More